TY - JOUR
AU - Li, Zhi-Kang
AB - Abstract Genetic load in the genome of the model species, rice, was genetically dissected by mapping quantitative trait loci (QTLs) affecting the radiosensitivity of 226 recombinant inbred lines (RILs) to γ-ray- and spaceflight-induced radiation. The parents and RILs varied considerably in their radiosensitivity to γ-ray irradiation. A total of 28 QTLs affecting the two index traits, seedling height (SH) and seed fertility (SF), of radiosensitivity were identified. The japonica parent, Lemont, was much more sensitive to γ-ray irradiation than the indica parent, Teqing, and its alleles at almost all QTLs were associated with increased radiosensitivity, suggesting a much higher genetic load in the japonica genome of rice. Six QTLs (QSh2a, QSh2b, QSh5a, QSh7, QSf3b, and QSf10b) were located in the genomic regions particularly sensitive to radiation and thus might represent possible ‘mutation hot spots’ in the japonica genome. Detailed characterization of these genomic regions may shed light on the evolution and subspecific differentiation of rice. Genetic load, mutagenesis, γ-rays, radiation damage, radiosensitivity, spaceflight Abbreviations Abbreviations SF seed fertility SH seedling height QTL quantitative trait locus RAPD randomly amplified polymorphic DNA RFLP restriction fragment length polymorphism SSR simple sequence repeat Introduction Genetic load, originally defined as the total amounts of potentially deleterious mutations in the genome of an organism (Muller, 1950), has been of tremendous interest to geneticists and evolutionary biologists because of its association with the adaptability of the organism to changing environments. Most plants contain a significant portion (more or less) of mutated genes in their genomes that are potentially harmful or even deleterious. Genetic load is believed to have played an important role in evolution and contributed frequently to the extinction of small populations (Kimura et al., 1963; Lynch et al., 1995). Because of their polyploid nature and the redundancy in gene copy number and function, most plants may not show reduced fitness despite the genetic load they carry. In plants, genetic load tends to be associated with outbreeders as compared with inbreeders because many deleterious mutations are masked in the heterozygous state (Wiens et al., 1987). The number of deleterious mutations and the genetic load in an organism's genome are difficult to measure directly because of the complexity of the genome. However, the amount of genetic load in a species' genome can be indirectly measured by its radiosensitivity, which is defined as the observable damage in fitness-related traits caused by mutagenic treatments. It is well known that radiosensitivity differs greatly among different plant species, subspecies, and even among different genotypes of the same species (Sparrow and Evans, 1961; Ukai, 1967; Ukai and Yamashita, 1980), indicating a tremendous variability in plants regarding genetic load in their genomes. In applied biology, particularly in agriculture, mutation breeding, i.e. generating mutations using chemical or physical treatments followed by selection for heritable changes of specific phenotypes, has achieved considerable success in genetic improvement of crop plants (Micke et al., 1985; Liu et al., 2004). Crop plants have been classified into three types, i.e. sensitive, intermediately sensitive, and non-sensitive based on their sensitivity to radiation (Takagi, 1974). Further studies indicated that radiosensitivity of several higher plants (tomato, peas, and rice) to γ-ray irradiation is a heritable trait (Davis, 1962; Blixt, 1969; Ukai, 1970), which is either under the control of Mendelian factors (Yamashita, 1964; Takaki and Yamashita, 1967) or of polygenic nature (Davis, 1962; Lawrence, 1963; Al-Rubeal and Godward, 1981). Thus, genetic load in plants can be dissected regarding its distribution and effects at individual genomic regions using the conventional quantitative trait locus (QTL) mapping approach (Paterson et al., 1988; Lander and Botstein 1989). Among many methods used to produce mutagenesis, γ-ray irradiation is the most common way to generate mutations in plants. It is well known that γ-ray treatment tends to cause random deletions of large genomic segments and serious phenotypic damage in treated plants, which are linearly related to the treatment dosage (Soriano, 1961; Nuclear Institute for Agriculture and Biology, 2003). Thus, the sensitivity of a plant to γ-ray irradiation provides an indirect measurement of the genetic load in its genome. The more sensitive to γ-ray irradiation a plant is, the larger the genetic load it has in its genome. Recently, treating plants in the space environment has become an increasingly common method of mutagenesis as part of many countries' space ambitions since the 1960s (Halstead and Dutcher, 1987; Horneck, 1992). Although not fully understood, the space radiation environment consists of a complex mixture of galactic cosmic radiation, trapped belt radiation, and solar particles (Badhwar, 1997). Of these, charged particles are the most important constituent of the radiation in the manned spacecrafts flying in a near-Earth orbit (∼400 km). Various biological effects on treated plants from exposure of the seed to space radiation, especially to the high-energy cosmic rays, have been documented, which include chromosomal aberrations (Krikorian and O'Connor, 1984; Slocum et al., 1984), developmental abnormalities (Bayonove et al., 1984; Kuang et al., 1996), and increased mutation rates (Kostina et al., 1984; Mei et al., 1994). In China, a total of 16 batches of biological materials (including dry seeds of different plant species, micro-organisms, etc.) have been sent into space in retrievable satellites and spacecrafts since 1987. Morphological mutations have been consistently observed in the treated materials, and these mutagenic effects derived from the treated plant seeds were apparently heritable (Bayonove et al., 1984; Kostina et al., 1984; Mei et al., 1994, 1998; Liu, 2000). Stable and promising lines with improved performance have been identified from the progeny of rice subjected to spaceflight and wheat seeds (Cyranoski, 2001; Dennis and Ding, 2002; Liu et al., 2004), even though the mechanism(s) of mutagenesis of the space environment on plants remain unclear. In the present study, the QTL mapping strategy was adopted to dissect genetically the amounts of genetic load in the rice genome that are responsible for the differential responses of two rice subspecies to γ-ray irradiation and space environment treatments. The results provided insights into the genomic distribution and magnitudes of genetic load associated with the subspecific differentiation of rice. Materials and methods Experimental materials Lemont (Oryza sativa ssp. japonica), a commercial semi-dwarf rice variety from southern USA, was used as the female parent to cross with Teqing (O. sativa ssp. indica), a high-yielding semi-dwarf variety from China. Two hundred and fifty-five F2 individuals were generated from the cross (Li et al., 1999) and allowed to self consecutively until F10, from which 292 recombinant inbred lines (RILs) were generated by single-seed descent (Xu et al., 2004). The parents, Lemont and Teqing, together with a random subset of 226 RILs were used as the materials in this study. Induction treatment and data collection Three hundred purified air-dried seeds from each of the RILs were divided into three equal parts. The first part was irradiated with 300 Gy of γ-rays at an exposure rate of 1.4 Gy min−1 from a 60Co source located in the Zhejiang Academy of Agricultural Sciences, China. The second part was carried by the Chinese spacecraft ‘Shen-Zhou 4’ launched in December of 2002 and travelled in a low-Earth orbit for 162 h. In the spacecraft, 100 seeds of each RIL were fixed with two layers of a plastic holder (6 cm×5 cm) and sandwiched between the nuclear track detectors that were used to measure the high energy cosmic rays around the seeds. The environmental parameters of the spacecraft included a flight altitude of 200–400 km, 63° inclination, 15–26 °C inner temperature, 1.5×10−4g microgravity, and 1.58 mGy of total radiation detected by an integrating thermal luminous dosimeter (TLD). It was estimated that all seeds were hit at least once by the moderate energy (Z/β ≥20) cosmic ray particles, and ∼80% of the seeds were hit at least once by the high energy (Z/β ≥50) cosmic ray particles based on the records on the nuclear track detectors. The third part of the seeds was kept as the untreated control. Here Z/β is an exploration parameter for measuring the level of energy in which Z is the atomic number of incident particles, and β is the ratio of the velocity of incident particles to the velocity of light. To measure the radiosensitivity of the RILs, all the treated and control RILs were sown in the concrete beds of the greenhouse in the Zhejiang Academy of Agricultural Sciences, Hangzhou, China during the summer of 2003 with three replications for each RIL/treatment. One week after sowing, 20 seedlings from each treatment were randomly selected to measure seedling height (SH, cm). Then, 30-d-old seedlings were transplanted into three-row plots at a spacing of 25×20 cm in the field (36 plants per plot for each of the treated and untreated RILs) with three replications for each of the RILs. The field was managed under standard procedures with three sprays of insecticides to control brown planthoppers (Nilaparvata lugens Stål.). At maturity, 15 main panicles were randomly sampled from 15 plants in each plot and the total number of spikelets per panicle was counted, which included the number of filled grains and that of unfilled grains. The seed fertility (SF, in %) was measured as the ratio of filled grains to the total number of spikelets per panicle. Linkage map construction Genotyping of the RILs with molecular markers and the construction of a complete linkage map were carried out in several steps, as described by Xu et al. (2004). The linkage map which was used for QTL analysis in this study (Fig. 2) consists of 164 well-distributed markers [40 restriction fragment length polymorphism (RFLP) markers, 100 simple sequence repeat (SSR) markers, 21 randomly amplified polymorphic DNA (RAPD) markers, and three morphological markers]. This map covered the whole rice genome with a total length of 1921 cM and an average distance of 11.7 cM between adjacent markers. Data analysis and QTL mapping Analysis of variance (ANOVA) was performed to evaluate differences in the induction treatments, among the RILs, and genotype by treatment interactions for the measured traits using SAS PROC GLM (SAS Institute, 1996). Least significant difference (LSD) tests were performed to compare the differences between the radiation-treated lines and untreated control for the parents and all RILs. Correlation between the traits in both treatments and between lines for the same traits across the treatments was determined using SAS PROC CORR (SAS Institute, 1996). Phenotypic data of the RILs, obtained from the control and two induction treatments, as well as trait differences between the treatments and control (treatment–control) of the RILs were used as input data to identify QTLs affecting SH, SF, and trait differences using the software QTLMAPPER v. 1.0 based on the mixed model approach (Wang et al., 1999). Specifically, significant markers (QTLs) associated with mean trait values of individual RILs were identified using stepwise regression with a threshold of P ≤0.005. Then, QTL parameters (locations, effects, and test statistics) of all putative QTLs were estimated using interval mapping and the restricted maximum likelihood estimation method, with all significant markers identified in the first step fixed in the model to control the background genetic variation. The permutation method (Churchill and Doerge, 1994) was used to obtain empirical thresholds for claiming QTLs of the experiment based on 1000 runs of randomly shuffling the trait values, which ranged from 2.50 for SF under the spaceflight treatment to 3.10 for SH in the controls. The use of a single arbitrary threshold in QTL mapping could easily detect a QTL in one treatment but not in another (Li et al., 2003). Thus, while all QTLs detected at the selected thresholds are presented, any QTLs detected in only one treatment was interpreted with caution. Also, to examine the extent to which inconsistent QTL detection across the treatments actually arose from type II errors, all QTLs identified in one treatment were re-examined using the data from the other treatments and the trait differences under the minimum threshold of P <0.05. In other words, when a QTL was identified using the data from the control experiment, this QTL was also tested by the data from the spaceflight, γ-ray irradiation, and differences between the treatments and control, and vice versa. The test statistics and QTL parameters associated with the QTL were also reported as long as the QTL reached the minimum threshold. Results Performance of the parents and RILs after induced treatments No significant differences between the parents were observed for SH and SF after the spaceflight (Table 1). γ-ray irradiation resulted in a significant reduction of 50% in SH and of 46% in SF for the japonica parent Lemont, but caused no apparent damage in the indica parent, Teqing. The RILs showed tremendous segregation for the two measured traits and differences between the induction treatments and control, which were approximately normally distributed (Table 2, Fig. 1), indicating that the responses of the RILs to the radiation treatments behaved like quantitatively inherited traits. ANOVA indicated highly significant differences among the treatments (R2=34.3 and 48.2% for SH and SF), among the RILs (R2=20.4 and 18.1%), and the genotype by treatment (R2=22.2 and 12.5%). γ-ray irradiation caused significantly reduced SH in 167 lines (74.2%) and significantly decreased SF in 141 lines (74.2%), resulting in a general reduction of 49.5% in SH and of 54.4% in SF of the RILs. Only two lines showed significantly increased SH and one line showed significantly increased SF after the γ-ray treatment. By contrast, spaceflight caused no shift in the mean SH of the RILs (Table 1; Fig. 1), but 13 of the RILs (5.8%) showed significantly improved seedling growth and 17 lines (7.5%) showed significantly reduced seedling growth, as compared with the controls. Similar to SH, spaceflight caused no change in the mean SF of the RILs (Table 1; Fig. 1), but 12 of the RILs (5.5%) showed significantly improved SF and nine lines (4.1%) had significantly reduced SF. Table 1 Performance of seedling height (SH) and seed fertility (SF) of the parents and the Lemont/Teqing RILs after different induction treatments   SH (cm)   SF (%)     Control  Spaceflight  γ-rays  Control  Spaceflight  γ-rays  Lemont  7.6±0.6  9.4±0.6  3.8±0.5***  89.2±2.6  88.0±4.8  48.1±7.0***  Teqing  11.0±0.5  9.9±1.0  9.1±0.8  92.9±1.3  93.9±1.8  89.1±7.1  RILs  9.1±2.4  9.3±2.0  4.6±2.2***  80.1±11.7  79.5±12.8  36.5±15.6***    SH (cm)   SF (%)     Control  Spaceflight  γ-rays  Control  Spaceflight  γ-rays  Lemont  7.6±0.6  9.4±0.6  3.8±0.5***  89.2±2.6  88.0±4.8  48.1±7.0***  Teqing  11.0±0.5  9.9±1.0  9.1±0.8  92.9±1.3  93.9±1.8  89.1±7.1  RILs  9.1±2.4  9.3±2.0  4.6±2.2***  80.1±11.7  79.5±12.8  36.5±15.6***  All values given are the means ±SD. Significance level of ***P <0.001 based on t-tests between the treatments and the control. View Large Table 2 Comparison of seedling height (SH) and seed fertility (SF) between spaceflight- and γ-ray-treated Lemont/Teqing RILs and untreated controls based on ANOVA/LSD tests Treatment  SH   SF     No. of lines evaluated  No. of lines with significantly increased trait value  No. of lines with significantly decreased trait value  No. of lines evaluated  No. of lines with significantly increased trait value  No. of lines with significantly decreased trait value  Spaceflight  226  13  17  219  12  9  γ-rays  225  2  167  190  1  141  Treatment  SH   SF     No. of lines evaluated  No. of lines with significantly increased trait value  No. of lines with significantly decreased trait value  No. of lines evaluated  No. of lines with significantly increased trait value  No. of lines with significantly decreased trait value  Spaceflight  226  13  17  219  12  9  γ-rays  225  2  167  190  1  141  View Large Fig. 1 View largeDownload slide Frequency distribution of seedling height (SH) and seed fertility (SF) detected under the control, spaceflight, and γ-radiation treatments, and their differences between the treatments and the control in the Lemont (P1)/Teqing (P2) RILs. Fig. 1 View largeDownload slide Frequency distribution of seedling height (SH) and seed fertility (SF) detected under the control, spaceflight, and γ-radiation treatments, and their differences between the treatments and the control in the Lemont (P1)/Teqing (P2) RILs. Correlation among tested traits after different induction treatments in the RILs The space-induced SF was highly and positively correlated with the SF of the control (r=0.81, P <0.0001), indicating that spaceflight had minimum effects on SF (Table 3). High and positive correlation (r=0.83, P <0.0001) was also observed between γ-ray-induced SF and its effects (trait difference) on SF, indicating that the damaging effect of γ-ray irradiation was quite consistent across all RILs. For SH, moderate and positive correlation (from 0.38 to 0.58) was observed between spaceflight or γ-ray irradiation treatment and the control, and between the two treatments, indicating there was some degree of similarity in the seedling responses of the RILs to the two radiation treatments (Table 3). Partial and negative correlation (r=−0.46 and −0.52) existed between the controls and the effects of radiation treatments on SH, indicating that lines with faster seedling growth tended to suffer less damage from the radiation treatments. Table 3 Correlation coefficients among seedling height (SH), seed fertility (SF), and differences betwen treatments and control (DSH and DSF) in Lemont/Teqing RILs after different induction treatments Trait  SHC  SFC  SHS  SFS  SHγ  SFγ  DSHS  DSFS  DSHγ  SFC  0.14*                    223                  SHS  0.58***  0.10                  226  223                SFS  0.08  0.81***  0.10                220  219  220              SHγ  0.38***  0.17*  0.40***  0.19**              225  222  225  219            SFγ  −0.01  0.13  0.09  0.09  0.04            192  190  192  187  192          DSHS  −0.46***  −0.05  0.46***  0.04  0.03  0.09          226  223  226  220  225  192        DSFS  −0.09  −0.14*  0.01  0.47***  0.04  −0.08  0.11        219  219  219  219  218  186  219      DSHγ  −0.52***  0.02  −0.13*  0.10  0.60***  0.04  0.42***  0.11      225  222  225  219  225  192  225  218    DSFγ  −0.10  −0.44***  0.06  −0.38***  0.02  0.83***  0.14  0.06  0.09    191  190  191  187  191  191  191  186  191  Trait  SHC  SFC  SHS  SFS  SHγ  SFγ  DSHS  DSFS  DSHγ  SFC  0.14*                    223                  SHS  0.58***  0.10                  226  223                SFS  0.08  0.81***  0.10                220  219  220              SHγ  0.38***  0.17*  0.40***  0.19**              225  222  225  219            SFγ  −0.01  0.13  0.09  0.09  0.04            192  190  192  187  192          DSHS  −0.46***  −0.05  0.46***  0.04  0.03  0.09          226  223  226  220  225  192        DSFS  −0.09  −0.14*  0.01  0.47***  0.04  −0.08  0.11        219  219  219  219  218  186  219      DSHγ  −0.52***  0.02  −0.13*  0.10  0.60***  0.04  0.42***  0.11      225  222  225  219  225  192  225  218    DSFγ  −0.10  −0.44***  0.06  −0.38***  0.02  0.83***  0.14  0.06  0.09    191  190  191  187  191  191  191  186  191  Significance levels of *P <0.05, **P <0.01, and ***P <0.001, respectively. The upper data are the correlation coefficients, and the lower data are the mean degrees of freedom for each pair of correlations. Superscripts C, S and γ on the right of the trait abbreviations represent control, spaceflight, and γ-ray irradiation treatments, respectively. View Large Identification of QTLs associated with radiosensitivity For SH, 18 QTLs were identified and mapped to 10 rice chromosomes except chromosomes 10 and 12, including eight detected in the untreated RILs, 13 detected in the γ-ray-treated RILs, nine detected in the spaceflight-treated RILs, and 12 by the trait differences between the treated and untreated lines (Table 4; Fig. 2). Based on their differential behaviours, these QTLs could be classified into seven types. Type 1 included QSh5a that was only detected in the control but not under the two treatments. Type 2 included QSh2c, QSh3b, QSh4a, QSh6, QSh8a, and QSh9, which were induced specifically by γ-ray irradiation. Type 3 included QSh8b which was induced specifically by spaceflight. Type 4 included QSh3c and QSh11b that were detected in both the control and γ-ray irradiation-treated groups. Type 5 included QSh3a, QSh5b, and QSh11a detected in both the control and spaceflight groups. Type 6 included QSh1and QSh4b that were detected in both the control and the two treatment groups. Type 7 included QSh2a, QSh2b, and QSh7 that were apparently induced by both treatments. Twelve QTLs (QSh1, QSh2a, QSh2b, QSh2c, QSh3b, QSh4b, QSh6, QSh7, QSh8a, QSh8b, and QSh9) contributed to SH differences of the RILs between the two treatments and control. All these QTLs except type 6 were expected to have arisen from the parental allelic diversity in radiosensitivity. Specifically, types 2, 3, and 7 represented QTLs at which the parental allelic differences were specifically induced by either or both induction treatments, and types 1, 4, and 5 represented QTLs where the parental allelic differences were specifically removed by either or both induction treatments. The Lemont alleles at all 18 loci resulted in reduced SH or increased SH sensitivity. Table 4 Twenty-eight QTLs affecting seedling height (SH) and seed fertility (SF) detected in the Lemont/Teqing RILs after mutation-inducing treatments Trait  QTL  Chromosome  Marker intervala  Parametersb  Controlc  γ-rays  Spaceflight  γ-rays–control  Spaceflight–control  SH  QSh1  1  RM129–RM24  LOD  2.42  2.01  11.94    5.48          Effect  −0.35  −0.34  −0.57    −0.38    QSh2a  2  OSR17–RM154  LOD    3.98  8.64    2.36          Effect    −0.47  −0.52    −0.30    QSh2b  2  RM27–RM324  LOD    6.35  2.58  2.94  5.25          Effect    −0.49  −0.36  −0.44  −0.42    QSh2c  2  OSR26–RM208  LOD    7.25    2.75            Effect    −0.54    −0.43      QSh3a  3  C636×–RG450  LOD  2.74    6.31              Effect  −0.37    −0.46        QSh3b  3  G06075–RM156  LOD    8.37    3.01            Effect    −0.54    −0.45      QSh3c  3  RM227–RM85  LOD  13.72  4.19                Effect  −0.61  −0.43          QSh4a  4  RM252–RM303  LOD    5.67                Effect    −0.45          QSh4b  4  Q05050–A17130  LOD  3.76  7.67  8.11  2.80            Effect  −0.44  −0.56  −0.54  −0.43      QSh5a  5  gl1–RM13  LOD  3.23                  Effect  −0.40            QSh5b  5  RM163–RM161  LOD  3.11    7.60              Effect  −0.39    −0.51        QSh6  6  OSR19–RZ516  LOD    3.02    3.61            Effect    −0.41    −0.48      QSh7  7  RM11–OSR4  LOD    3.83  2.87    2.42          Effect    −0.46  −0.37    −0.31    QSh8a  8  CSU754–G104  LOD    5.30    3.03            Effect    −0.50    −0.47      QSh8b  8  OSR7–RM230  LOD      3.22    2.20          Effect      −0.41    −0.28    QSh9  9  RM201–RM215  LOD    3.78    3.72            Effect    −0.46    −0.49      QSh11a  11  RM167–RZ53  LOD  6.46    2.76              Effect  −0.47    −0.36        QSh11b  11  RM229–RM21  LOD  3.42  3.99                Effect  −0.41  −0.47        SF  QSf3a  3  RM16–RZ403b  LOD  5.38    8.62              Effect  −2.56    −3.28        QSf3b  3  RM227–RM85  LOD  3.89                  Effect  −2.42            QSf6  6  RM50–RM276  LOD    3.67    2.14            Effect    −4.05    −3.3      QSf7  7  RM234–CDO405  LOD  8.35  3.83  2.22  2.40            Effect  −3.90  1.27  −2.89  4.93      QSf8  8  CSU754–G104  LOD  5.20    3.24              Effect  −2.54    −2.54        QSf9  9  J01090–RZ698  LOD  2.24  2.74      3.16          Effect  −1.83  −3.35      1.48    QSf10a  10  RM271–RZ400  LOD  2.43    6.10    3.17          Effect  −1.89    −2.79    −1.46    QSf10b  10  RG1094f–RM228  LOD  3.26                  Effect  −2.24            QSf12a  12  RM260–RG20q  LOD    2.20    3.21            Effect    −3.20    −4.00      QSf12b  12  RM17–G1106  LOD  3.81    2.88              Effect  −2.38    −2.37      Trait  QTL  Chromosome  Marker intervala  Parametersb  Controlc  γ-rays  Spaceflight  γ-rays–control  Spaceflight–control  SH  QSh1  1  RM129–RM24  LOD  2.42  2.01  11.94    5.48          Effect  −0.35  −0.34  −0.57    −0.38    QSh2a  2  OSR17–RM154  LOD    3.98  8.64    2.36          Effect    −0.47  −0.52    −0.30    QSh2b  2  RM27–RM324  LOD    6.35  2.58  2.94  5.25          Effect    −0.49  −0.36  −0.44  −0.42    QSh2c  2  OSR26–RM208  LOD    7.25    2.75            Effect    −0.54    −0.43      QSh3a  3  C636×–RG450  LOD  2.74    6.31              Effect  −0.37    −0.46        QSh3b  3  G06075–RM156  LOD    8.37    3.01            Effect    −0.54    −0.45      QSh3c  3  RM227–RM85  LOD  13.72  4.19                Effect  −0.61  −0.43          QSh4a  4  RM252–RM303  LOD    5.67                Effect    −0.45          QSh4b  4  Q05050–A17130  LOD  3.76  7.67  8.11  2.80            Effect  −0.44  −0.56  −0.54  −0.43      QSh5a  5  gl1–RM13  LOD  3.23                  Effect  −0.40            QSh5b  5  RM163–RM161  LOD  3.11    7.60              Effect  −0.39    −0.51        QSh6  6  OSR19–RZ516  LOD    3.02    3.61            Effect    −0.41    −0.48      QSh7  7  RM11–OSR4  LOD    3.83  2.87    2.42          Effect    −0.46  −0.37    −0.31    QSh8a  8  CSU754–G104  LOD    5.30    3.03            Effect    −0.50    −0.47      QSh8b  8  OSR7–RM230  LOD      3.22    2.20          Effect      −0.41    −0.28    QSh9  9  RM201–RM215  LOD    3.78    3.72            Effect    −0.46    −0.49      QSh11a  11  RM167–RZ53  LOD  6.46    2.76              Effect  −0.47    −0.36        QSh11b  11  RM229–RM21  LOD  3.42  3.99                Effect  −0.41  −0.47        SF  QSf3a  3  RM16–RZ403b  LOD  5.38    8.62              Effect  −2.56    −3.28        QSf3b  3  RM227–RM85  LOD  3.89                  Effect  −2.42            QSf6  6  RM50–RM276  LOD    3.67    2.14            Effect    −4.05    −3.3      QSf7  7  RM234–CDO405  LOD  8.35  3.83  2.22  2.40            Effect  −3.90  1.27  −2.89  4.93      QSf8  8  CSU754–G104  LOD  5.20    3.24              Effect  −2.54    −2.54        QSf9  9  J01090–RZ698  LOD  2.24  2.74      3.16          Effect  −1.83  −3.35      1.48    QSf10a  10  RM271–RZ400  LOD  2.43    6.10    3.17          Effect  −1.89    −2.79    −1.46    QSf10b  10  RG1094f–RM228  LOD  3.26                  Effect  −2.24            QSf12a  12  RM260–RG20q  LOD    2.20    3.21            Effect    −3.20    −4.00      QSf12b  12  RM17–G1106  LOD  3.81    2.88              Effect  −2.38    −2.37      a The underlined markers are those closer to the true QTL positions. b The log-odds ratio (LOD) is the test statistics defined as: LOD=−log (L0/L1). The thresholds of LOD=2.50 and 3.10 for SH and SF are equivalent to P ≤0.0007 and 0.00016, respectively (Lander and Botstein, 1989). c Underlined data are the parameters of the QTL detected under the threshold of P <0.05. QTL effects were associated with the Lemont allele. View Large Fig. 2 View largeDownload slide The molecular linkage map and the location of the main-effect QTLs affecting seedling height (SH) and seed fertility (SF) detected in the RILs in the controls and in the two treatment groups. Markers starting with RG, RZ, R, CDO, G, C, Y and CSU are RFLP markers. RM (or OSR) and RD represent SSR and RAPD markers. Fig. 2 View largeDownload slide The molecular linkage map and the location of the main-effect QTLs affecting seedling height (SH) and seed fertility (SF) detected in the RILs in the controls and in the two treatment groups. Markers starting with RG, RZ, R, CDO, G, C, Y and CSU are RFLP markers. RM (or OSR) and RD represent SSR and RAPD markers. For SF, 10 QTLs were identified and mapped to seven rice chromosomes, including eight detected in the control, four by γ-ray irradiation, five by spaceflight, and five by the trait differences between the radiation treatments and control (Table 4; Fig. 2). These included QSf3b and QSf10b of type 1 that were detected only in the control, QSf6 and QSf12a of type 2, which were induced by γ-ray irradiation, QSf9 of type 4 that were expressed in the control and γ-ray irradiation-treated groups, QSf3a, QSf8, QSf10a, and QSf12b of type 5 that were expressed in the controls and those undergoing spaceflight, and QSf7 of type 6 that were expressed in the control and after treatments. The Lemont alleles at all these loci except QSf7 under γ-ray irradiation were associated with reduced fertility or increased SF sensitivity. Five QTLs (QSf6, QSh10a, QSh12a, QSf7, and QSh9) contributed to SF differences of the RILs between the radiation treatments and control, and the Lemont alleles at QSf6, QSh10a, and QSh12a showed a reduced SF difference, whereas the Teqing alleles at QSf7 and QSh9 were associated with a reduced SF difference. Discussion A high genetic load in japonica subspecies and its implications Although the amounts of genetic load in plant genomes are a subject of general interest and have been reported in many species (Barrett and Charlesworth, 1991; Kirkpatrick and Jarne, 2000), this study represents the first effort to understand its magnitude and genomic distribution in the model plant, rice, using molecular markers. The tremendous differences in radiosensitivity, measured as phenotypic changes or damage in SH and SF between the radiation-treated seeds and untreated control, between the parents and among RILs observed in this study were expected to have resulted from two factors: (i) the direct effects of radiation on DNA molecules in the seeds and expression of the affected genes; and (ii) the buffering abilities in trait expression of individual lines involved. At the genomewide level, the former were the direct effects of mutagenesis arising from the γ-irradiation treatment, and was expectedly random in the sense that every gene in the genomes of individual lines had an equal chance to be affected. The latter could be considered as the indirect measurements of genetic load in the genomes of individual RILs that varied considerably. The large difference in radiosensitivity between the parents observed in this study indicated that the japonica parent, Lemont, has a much higher genetic load in its genome than the indica parent, Teqing. Consistent with this result, the Lemont alleles at almost all identified QTLs were associated with increased radiosensitivity (Table 4). In a separate study, it was found that the Teqing alleles at ∼50% of the loci across the genome, particularly chromosome 7, were favoured in both the indica (Teqing) and japonica (Lemont) genetic backgrounds without apparent selection (unpublished observations), which is generally true for all populations derived from indica/japonica crosses (Lyttle, 1991; Lin et al., 1992; Nakagahra, 1996; Xu et al., 1997). Empirical practices in mutation breeding indicate that the recommended dosage of γ-ray treatment to achieve the desirable level of mutation is much higher for indica varieties (250–350 Gy) than that for japonica lines (200–300 Gy) (IAEA, 1977). Consistent with this observation, recent comparative genomic sequence analyses of rice chromosomes 4 and 10 clearly indicate that there are many more insertions in the Nipponbare (japonica) genome than in the indica genomes of Guang-Lu-Ai 4 and 9311, due primarily to activities of transposable elements (Feng et al., 2002; Goff et al., 2002; Yu et al., 2002). All these results lead us to the conclusion that the japonica genome has a much higher genetic load than the indica genome, and the evolution of the japonica subspecies was associated with loss of function at many loci arising primarily from insertions of transposable elements. Thus, the observed low level of genetic variation and greater genetic load in almost all japonica varieties (Glaszmann, 1987; Zhang et al., 1992; Li and Rutger, 2000) suggest that evolution of japonica rice probably went through severe genetic bottlenecks, which must have happened during domestication, as suggested by the multilocus structure of isozyme variation (Li and Rutger, 2000). Given the large difference in radiosensitivity to γ-rays between the parents, all identified QTLs, except for QSh1, QSh4b, and QSf7 of type 6, represented the genomic regions where the parental allelic diversity was associated with the parental difference in radiosensitivity. Of these, six QTLs are of particular interest, including QSh5a, QSf3b, and QSf10b of type 1 at which functions of the Teqing alleles appeared to have been knocked-out by both γ-ray and spaceflight treatments, and QSh2a, QSh2b, and QSh7 of type 7 that were induced by both γ-ray and spaceflight treatments. Because of the random nature of the radiation treatments, the six QTLs appeared to be in the genomic regions particularly sensitive to radiation and thus might represent possible ‘mutation hot spots’ in the japonica genome. It is conceivable that at least three types of genes might have been involved with the identified QTLs, even though they were indistinguishable by phenotype. The first type could be those whose functions were interrupted directly by the radiation treatments. The second type included those that could compensate in function for the first type. The third type included those affecting the ability to repair induced DNA damage. The third type of genes are more important to determine whether the induced DNA damage will become heritable mutations. Compared with γ-ray irradiation-sensitive varieties, resistant varieties tend to show less damage owing to their better ability to repair the damage (Inoue, 1980). Thus, it would be of great interest to distinguish the identified QTLs with regard to the types of genes they actually represent. Further study is underway to determine which of these identified QTLs are involved in heritable mutations. Comparison between γ-rays and spaceflight as mutagenic treatments In this study, severe damage in the parents and RILs caused by γ-ray irradiation were consistent with the fact that 11 (39.3%) of the identified QTLs for radiosensitivity (QSh2a, QSh2b, QSh2c, QSh3b, QSh4a, QSh6, QSh7, QSh8a, QSh9, QSf6, and QSf12a) were associated with the γ-ray treatment. These QTLs, detected under γ-ray irradiation but not in the control, indicated that the differences between alleles of Lemont and Teqing were not significant in the control but were significant under the treatment, which may result from knock-out of functions of the Lemont alleles at these loci by γ-ray irradiation. By contrast, the spaceflight treatment appeared to have small effects on the treated materials and there were only four QTLs (QSh2a, QSh2b, QSh7, and QSh8b) at which functions of the Lemont alleles were apparently disrupted by spaceflight. No significant differences in magnitude of QTL effects were found between QTLs specifically induced by γ-rays and those induced by spaceflight (Table 4), indicating that more genomic regions were responsible for the greater damage caused by γ-rays than by spaceflight. Thus, the phenotypic effects of the space treatment on rice observed in this study and reported in other plant species (Bayonove et al., 1984; Kostina et al., 1984; Braam et al., 1997; Hampp et al., 1997; Mei et al., 1998) appear to resemble very closely those treated by the chronic low-dose external ionizing radiation reported previously (Zaka et al., 2002; Sahr et al., 2005), but continuous efforts should be made in order to determine if the space treatment is a unique and cost-effective way for mutation induction as compared with other types of mutagenesis. Supplementary material The marker information and the seeds of Lemont/Teqing RILs are freely available. Interested persons should contact Dr Zhi-Kang Li or Dr Pinson (USDA-ARS, sr-pinson@tamu.edu) directly for the materials and related marker information. We are very grateful for the valuable comments and suggestions on the earlier versions of the manuscript from the two anonymous reviewers and Dr Luxiang Liu of the Chinese Academy of Agricultural Sciences. This project was supported by grants of ‘Application of Aerospace Mutation in Rice Breeding’ and ‘Obtaining and Analyzing Techniques of Space Biological Information’ from the 863 Programs of the Ministry of Science and Technology of China. 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Published by Oxford University Press [on behalf of the Society for Experimental Biology]. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org
TI - Heavy genetic load associated with the subspecific differentiation of japonica rice (Oryza sativa ssp. japonica L.)
JF - Journal of Experimental Botany
DO - 10.1093/jxb/erl046
DA - 2006-07-25
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EP - 2824
VL - 57
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DP - DeepDyve
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